DRAFT

1 Overview

Currently, there is no place where the public can readily find answers to general questions regarding the state of salmon in BC and the Yukon. It is difficult to find credible answers to questions such as:

How are salmon populations doing?
What are the major factors influencing the state of salmon?
What is being done - and what could be done - to mitigate threats and recover salmon populations and the habitats they depend on?

This lack of publicly accessible information about the state of Pacific salmon and opens the door to misinformation that can hinder and harm our ability to conserve salmon.

The Pacific Salmon Foundation (PSF) is developing an inaugural State of Pacific Salmon in Canada publication (henceforth referred to as SPS), which will address these questions and be tailored to the questions of a salmon-informed public audience. The outputs will be updated regularly (e.g. annually or biennially) to reflect new data and changes to the state of Pacific salmon populations.

As part of the SPS report, PSF’s Salmon Watersheds Program is developing simple metrics to report on the status and trends in regional abundance for each species. This document outlines the data sources and analytical approach used to estimate time series of spawner abundance and, where available, run size for each species and region, as well as draft results. It is intended to be shared with external partners in order to garner feedback on preliminary results that will inform our approach moving forward. Our analyses are ongoing, and the information here is subject to change on a regular basis, but we will endeavor to keep it up-to-date.

2 Methods

2.1 General Approach

We report spawner abundance for each of nine regions that represent all major Pacific salmon-bearing watersheds in Canada: Yukon, Transboundary, Nass, Skeena, Haida Gwaii, Central Coast, Vancouver Island & Mainland Inlets, Fraser, and Columbia. These regions are also used to organize data in the Pacific Salmon Explorer. There are a relatively small number of Pacific salmon that spawn in the MacKenzie River basin in Arctic Canada that are currently not considered here.

We separate five species of Pacific salmon: Chinook, chum, coho, pink, and sockeye. We also report status of steelhead trout for regions where spawner data are are available (Nass, Skeena, and interior Fraser). We note that when assessing biological status, pink are often separated into even- and odd-year lineages due to their consistent 2-year life cycle. However, for the general overviews provided in State of Salmon we consider generational averages, which take the running average of even- and odd-year lineages for pink salmon. This approach of using generation running averages also smooths over dominant lines for sockeye salmon, for which many populations display cyclic dominance. Shifts in dominance between even- and odd-year pink populations or declines in sub-dominant years of sockeye salmon are considered in a more nuanced way when discussing how changes in abundance have been reflected in the diversity and distribution of each species within the region.

For each of these species, where data are available, we construct an index of spawner abundance at the regional scale. We focus on spawner abundance, rather than catch or run size (i.e., catch plus spawners), because data on spawner abundance are more readily summarized at different spatial scales. Further, spawners represents the abundance of salmon available for to meet cultural and ecological needs and thus provides a measure of status relevant to communities and ecosystems, rather than industry. We recognize that commercial catch has historically been a substantial portion of salmon that return to the coast, and that ignoring declines in catch will underestimate the declines in overall salmon abundance. Therefore, we are looking to include information on run size for species and regions with reliable data. This assessment of the data landscape is ongoing, and one of the reasons why the results here should be considered preliminary.

For some species and regions, there is a reliable source of data for region-wide escapement and/or run size that we can directly use as an index of abundance. For example, stocks that are governed by international treaties may be monitored by the Pacific Salmon Commission, and tend to have reliable time series of abundance available at regional scales. These data sources are outlined in the Region-Specific Data section. For species and regions where aggregate abundance is not reported at the scale needed, we adapted our approach to make the best use of available data. In most cases, this meant expanding spawner abundance from stream-level estimates to get a regional scale index of spawner abundance using two types of expansion factors (English et al. 2016). This expansion process is described in more detail below.

R code for the analyses described here are available on GitHub at https://github.com/salmonwatersheds/state-of-salmon.

2.2 Region-Specific Data

Here we describe specific data sources for the abundance of salmon and steelhead at the regional scale. If no data source is listed for a species and region, then we took an alternate approach of estimating regional-scale abundance from finer-scale data (see Expansion Factors).

2.2.1 Yukon

The Canadian portion of the Yukon River is home to Chinook, chum, and coho salmon. Border escapement and run size of Canadian-origin Chinook and fall chum salmon is estimated at the Eagle Sonar station on the Yukon/Alaska border, and are available from the Yukon River Panel in their Joint Technical Committee (JTC) Reports. Specifically, we used Chinook “Spawning escapement estimate” and “Canadian origin total run size estimate” from Appendix B11 of Yukon River Joint Technical Committee (2023). Chum salmon data were from Appendix B16 of Yukon River Joint Technical Committee (2023). There are also Chinook, chum, and coho salmon in the Canadian portion of the Porcupine River, which joins the mainstem Yukon River in Alaska. Data on escapement to the Porcupine River are more patchy and not currently included here.

2.2.2 Transboundary

For Chinook, coho, and sockeye, we assessed regional abundance in the Transboundary using estimates of border escapement provided in the Pacific Salmon Commission’s Joint Transboundary Technical Committee Reports. The regional spawner abundance and run size of Chinook was calculated as the sum of escapement and run size, respectively, to the Stikine, Alsek, and Taku watersheds, available from Table B2 of Joint Chinook Technical Committtee (2023) and provided to E. Hertz in Excel format following a data request. Sockeye spawner abundance and run size was the sum escapement or Terminal Run, respectively, from the Stikine River (Appendix B21 (Transboundary Technical Committee 2022)) and the Taku River (Appendix D15; (Transboundary Technical Committee 2022)). Coho spawner abundance and run size are available for the Taku River only, taken from Appendix D20 of Transboundary Technical Committee (2022).

Pink and chum salmon abundance are less monitored in the Transboundary, with ongoing escapement available only from the Canyon Island fish wheel on the Taku River. We used the index of escapement from the Canyon Island fish wheel as an index of regional spawner abundance (Transboundary Technical Committee 2022). There are some historical data for pink salmon spawner abundance in the Nakina River of the Taku watershed, but this location has not reported data since 1998 and thus we did not include this stream survey in our index of regional abundance.

We recognize that our approach in the Transboundary lacks information from many unmonitored watersheds, in particular the smaller watersheds of the Chilkat, Unuk, and Whiting Rivers. In the absence of better monitoring, we choose to report available data as a proxy for regional abundance, and note that the contribution of these smaller watersheds to total regional abundance for each species is likely small.

2.2.3 Nass

Nass sockeye escapement and run size were extracted from Pacific Salmon Commission reports Standing Committee on Scientific Cooperation (2023).

The index of Nass steelhead spawner abundance is based on estimated spawner abundance to the Nass Summer CU, provided by the Nisga’a Fish and Wildlife Department and LGL Ltd. (publication forthcoming). There is another steelhead CU in the Nass region - Nass Winter - which is not well monitored and therefore not included in our index of spawner abundance.

2.2.4 Skeena

Skeena sockeye escapement and run size were extracted from Pacific Salmon Commission reports Standing Committee on Scientific Cooperation (2023).

The index of Skeena steelhead spawner abundance is derived from estimated escapement of Skeena Summer steelhead at the Tyee Test Fishery (1956 - present), provided by the Province. As for other regions, these estimates may not capture winter-run steelhead, for which data are not available.

2.2.5 Fraser

River-level spawner abundance for Fraser Chinook found in NuSEDS can be highly unreliable, thus we based our expansions on only the most intensively monitored streams that have been deemed as reliable estimates of abundance by Brown et al. (2020). These streams are those shown in the Pacific Salmon Explorer, and the associated data can be accessed from the stream survey data in the SWP Data Library. We also truncated the Chinook index of abundance to 1995 and more recent data, since earlier years have been deemed unreliable (Brown et al. 2020).

Data on aggregate abundance of pink salmon and sockeye salmon in the Fraser region are provided by the Pacific Salmon Commission (PSC), and accessed through the Fraser Panel Annual Report: Data Application. For these species, we report on the run size and escapement (or ‘spawning escapement’ for sockeye). We note that Fraser River pink salmon are only counted in the dominant, odd-year run.

We are currently exploring the possibility of including regional-scale abundance for Fraser Chinook, coho, and chum from DFO and/or the PSC, but have not yet acquired these data.

Steelhead trout in the Fraser are monitored by the Province in at least 10 different streams, but these data are not readily available. Relatively reliable estimates of steelhead spawner abundance are available at the CU-level for interior Fraser steelhead from the Thompson Summer CU (monitored at the Thompson River) and Mid Fraser Summer CU (primarily monitored at the Chilcotin River). We used the sum of CU-level spawner abundance for these two CUs as an index of Fraser steelhead abundance. We note that this approach does not include more coastal populations, such as the Lower Fraser Summer steelhead monitored in the Coquihalla River or Boundary Bay Winter steelhead, which may not have declined to the same extent over the past decade. However, a lack of publicly accessible data on coastal Fraser steelhead has limited our ability to include these CUs in our index of abundance.

2.2.6 Columbia

We used CU-level estimates of spawner abundance (run reconstructions) sourced from DFO (Ogden, pers. comm.) for Chinook and Stockwell and Hyatt (2003) and subsequent updates for sockeye.

There is no monitoring of steelhead trout in the Canadian portion of the Columbia region, but the Okanagan Nation Alliance does enumerate steelhead in akskwəkwant (Inkaneep Creek) and estimate a Canadian portion of steelhead spawning abundance. These data can be found in associated report (e.g. Okanogan Basin Monitoring and Evaluation Program (2023)) and are available in the Pacific Salmon Explorer as the CU-level spawner abundance for the Mid Columbia Summer CU.

2.3 Expansion Factors

For species and regions that lacked reliable data on spawner abundance and run size at the appropriate scale, we estimated regional-scale abundance from stream-level surveys. We started with spawner survey data shown in the Pacific Salmon Explorer. Spawner surveys were each assigned to one of the nine regions we considered based on their geographic location. We note that this is slightly different from how spawner survey data are organized in the Pacific Salmon Explorer, where data are organized by Conservation Units (CUs) that may span regional boundaries (e.g. for pink salmon that have relatively geographically large CUs). In cases of trans-regional CUs, the spawner surveys appear in both regions in the Pacific Salmon Explorer, whereas here we assign spawner surveys to the region in which they fall geographically, regardless of the CU boundary.

Spawner survey data are largely derived from river-level estimates in DFO’s New Salmon Escapement Database System (NuSEDS), but are cleaned up to address issues of, for example, inconsistent naming of streams through time or duplicate data. The spawner survey abundance is equal to the MAX_ESTIMATE in NUSEDS for each year and river population, calculated as the maximum of all fields containing spawner abundance data (e.g. natural adult spawners, natural jack spawners, total broodstock removals). Each of these river populations has been designated as an indicator stream or non-indicator. Indicator streams are observed more consistently in recent decades, tend to have higher spawner abundance, and tend to be monitored using more intensive methods that provide greater accuracy (English et al. 2016). For further information on the compilation of spawner survey data, see the Pacific Salmon Explorer Technical Report.

Expansion Factor 1, \(F_{1,y/d}\), expands the observed spawner abundances in indicator streams to account for indicator streams that are not monitored in a given year. It is calculated for each year \(y\) of the spawner time series, and relies on a decadal contribution of each indicator stream to the total escapement to all indicator streams, \(P_{d,i}\) in decade \(d\) (English et al. 2016). The calculation of this decadal contribution requires at least one estimate from each indicator stream for the decade. If a decade does not contain sufficient information (i.e. one or more indicator streams are not monitored at all in a decade), then a reference decade is used to calculate \(P_{d,i}\). This reference decade is chosen to be: (1) the closest decade (historical or future) with sufficient information, or failing (1), (2) the 20-year period from 1980-1999 (Challenger et al. 2018).

For each decade (or reference decade if insufficient information) \(d\), the average number of spawners returning to indicator stream \(i\) is calculated as:

\[\bar{S}_{d,i} = \sum_{y = 1}^{Y_{d,i}} \frac{\hat{S}_{y/d, i}}{Y_{d,i}} \] where \(Y_{d,i}\) is the number of years for which spawner estimates are available within decade \(d\) for stream \(i\). From the average number of spawners for all indicator streams, the decadal proportional contribution of each indicator stream is calculated as:

\[P_{d,i} = \frac{\bar{S}_{d,i}}{\sum_{i=1}^{I} \bar{S}_{d,i}}\] where \(I\) is the total number of indicator streams.

Expansion Factor 1 is then calculated for each year within the decade \(y/d\) based on the decadal contributions and which streams were monitored or not in a given year:

\[F_{1,y/d}=\left( \sum_{i=1}^I P_{d,i} w_{y/d,i} \right)\] where \(w_{y/d,i}\) is 1 is stream \(i\) was monitored in year \(y\) and 0 if stream \(i\) was not monitored in year \(y\). Expansion Factor 1 is then multiplied by the sum of the observed spawners in all indicator stream to yield the expanded estimate of spawner abundances in all indicator streams in the region:

\[S'_{y} = F_{1,y/d} \sum_{i=1}^I \hat{S}_{y,i}\]

Expansion Factor 2 \(F_{2,d}\) expands the spawner abundance to all indicator streams, \(S'_{y}\), to account for non-indcator streams. Unlike Expansion Factor 1, this is calculated for each decade (rather than each year) and then applied to all years within a decade. Like Expansion Factor 1, there needs to be sufficient information within the given decade in order to calculate \(F_{2,d}\), or else a reference decade is chosen. See English et al. (2016) for detailed on how reference decades are chosen in that case.

Expansion Factor 2 is calculated as:

\[F_{2,d} = \frac{\sum_{i = 1}^I \bar{S}_{d,i} + \sum_{j = 1}^{J} \bar{S}_{d,j}}{\sum_{i = 1}^I \bar{S}_{d,i}}\] where \(\bar{S}_{d,i}\) and \(\bar{S}_{d,j}\) are the deacdal average number of spawners in indicator and non-indicator streams, respectively, calculated above. \(J\) is the total number of non-indicator streams. The adjusted total number of spawners in both indicator and non-indicator streams is then calculated as: \[ S''_{y} = F_{2,d} S'_{y} \]

Note that when expanding spawner abundance for spawner-recruit analysis, a third expansion factor is applied to account for streams that are never monitored and for observer (in)efficiency (Peacock et al. 2020). We did not apply this third expansion factor because it is highly undertain and we are interested in relative changes in abundance through time, so we do not require to expand to absolute abundance.

2.4 Smoothing

The expanded spawner abundance was smoothed using a right-aligned running geometric mean over the length of a generation. This reduces the influence of dominant years on the index of abundance and produces an index that is less sensitive to stochastic interannual variability that is common in salmon population dynamics. The generation length depends on species and region, and is based on the dominant life-history type for each species in a particular region. The smoothed spawner abundance in year \(y\) given a generation length \(g\) is calculated as: \[ \bar{S_{y}} = \left( \prod_{t = y-g+1}^y {S''_t} \right)^{1/g} \]

If there were missing data in a generation, the smoothed abundance was still calculated using the available years (i.e. ignoring the missing data, with the exponent \(1/g\) adjusted so that \(g\) reflected the number of years with data in the generation).

When plotting, we show the smoothed spawner abundance relative to the long term historical average, so that species that have vastly different abundances within a region can be plotted on the same y-axis for comparison.

2.5 Quantifying change

We summarize the time series of smoothed spwaner abundance at the regional scale using three different metrics of change:

Change from historical average
Change from the previous generation
Change over the entire time series

The first two metrics provide information on how the most recent spawner abundance compares to past values, while the third metric is a long-term trend in abundance that is less sensitive to the current index of abundance.

The percent change from historical average is calculated as: \[ (\bar{S_{y}} - \bf{S}) / \bf{S} \] where \(y\) is the most recent year for which the index of spawner abundance could be calculated and \(\bf{S}\) is the average smoothed spawner abundance over the entire time series.

The percent change from the previous generation is calculated as: \[ (\bar{S_{y}} - \bar{S}_{y-g} ) / \bar{S}_{y-g} \]

The change over the entire time series is calculated following the recommendations of D’Eon-Eggertson, Dulvy, and Peterman (2015), who found that the correct identification of declines in salmon population abundance may be most reliable when considering the entire time series, and applying regression-based estimates of change calculated from log-transformed and smoothed spawner abundances. As such, we fit a simple linear model to the time series of \(\log ( \bar{S}_{y} )\) over \(y\), and extract the model-predicted log abundance at the beginning, \(\log (\hat{S}_1)\), and end, \(\log(\hat{S}_n)\) of the time series. We then calculate the percent change between the predicted spawner abundances as:

\[ (\hat{S_{n}} - \hat{S}_{1} ) / \hat{S}_{1} \]

Above: Simulated time series of smoothed spawner abundance (black line), illustrating the three metrics of change that we report: a) Change from historical average (horizontal dashed line), b) Change from the previous generation (open point), and c) Change over the entire time series. In each case, the change is indicated by the red arrow.

3 Results

NOTICE!

These are PRELIMINARY results and the following figures should not be taken as a statement on the status of salmon in these regions. At this time, we are working to verify datasets and refine our methods.

3.1 All regions

The following table summarizes the three metrics of change (see Quantifying change) and the index spawner abundance in the current generation, previous generation, and long-term average for all regions and species. The table is sort-able (click on the header to sort increasing or decreasing), filterable by species or region (enter text in boxes below header), and searchable (enter text in top right search box).

3.2 Yukon

Coming soon!

3.3 Transboundary

Coming soon!

3.4 Nass

Highlights

While most species show declines over both long- and short-time frames, Nass pink salmon have rebounded to above historical average abundance in the most recent generation.
Chinook salmon have shown the biggest declines from historical average abundance (-61.6%) with declines persisting in the most recent generation.
Although sockeye have had a declining trend over the most recent generation, they are above historical average with an uptick in the most recent year.

Above: Index of spawner abundance through time for Pacific salmon in the Nass region. Spawner abundance is smoothed using a one-generation geometric running average (right-aligned), and plotted relative to the historical average for each species. Closed points highlight the most recent index of spawner abundance and open points indicate the value of the index one generation prior (note: generation length differs by species). The percent shown is the percent change from the historical average to the most recent year.